Natural Language Processing

We set accuracy targets against a baseline from your existing manual process rather than generic benchmarks, because the only number that matters is whether the model performs better than what it replaces. For classification tasks like ticket routing or sentiment detection, our fine-tuned models typically reach 90 percent or above on domain-specific data. We use F1 score for classification and entity tasks, BLEU for translation, and human review for conversational quality.

We fine-tune pre-trained language models on your organization's own documents and labeled examples rather than relying on general-purpose models that have never encountered your industry's vocabulary. This ensures the system correctly handles product names, internal codes, regulatory abbreviations, and domain-specific phrases. For clients with limited labeled data, we apply few-shot and transfer learning to get strong performance from a smaller training set.

An NLP model handles language understanding — classifying intent, extracting entities, detecting sentiment. A chatbot is the full application built around it: conversation design, dialogue management, fallback and escalation logic, and the integrations that let the bot fulfill requests. At Buoyancy, we build both layers together because a well-trained model inside a poorly designed conversational experience still fails the people using it daily.

We design NLP architectures around your privacy constraints from day one. For data that can leave your environment, we use cloud APIs like Azure Cognitive Services and OpenAI. For sensitive data — PII, clinical records, confidential contracts — we deploy self-hosted open-source models on your own infrastructure so nothing leaves your network. We also implement field-level masking, audit logging, and retention policies where compliance frameworks require it.